Connect Your App to Amazon Quick with MCP: 6-Step Integration Guide for ISVs

Connect Your App to Amazon Quick Using the Model Context Protocol (MCP)

TL;DR

  • Model Context Protocol (MCP) lets AI agents call your app’s capabilities as well-documented tools while preserving customer control and security.
  • Amazon Quick acts as an MCP client: it discovers tools (listTools) and invokes them (callTool). Build an MCP server, support HTTPS + streaming, and design for a 300-second operation limit.
  • Follow a six-step checklist—deploy, implement, secure, document, register, test—and operate the endpoint like a production API (logging, throttles, versioning).

Who should read this: ISV product leads, platform engineers, cloud architects, and security teams planning an Amazon Quick integration or opening app capabilities to AI agents and automations.

What MCP is and why Amazon Quick customers care

Model Context Protocol (MCP) is a lightweight contract that exposes application capabilities (tools) so AI agents can discover and call them. Think of an MCP tool like a well-documented API endpoint a model can call — with guardrails for auth, inputs, outputs, and streaming behavior.

Amazon Quick acts as an MCP client (the caller). Your MCP server is the service that exposes tools. When agents within Quick need to act—fetch data, execute transactions, or enrich a knowledge base—Quick uses discovery (listTools) and invocation (callTool) flows to integrate with your application under customer governance.

Prerequisites

  • Customer must have an Amazon Quick Professional subscription and a user with Author (or higher) permissions to register integrations.
  • Your MCP server must be reachable over public HTTPS and support either Server-Sent Events (SSE) or HTTP streaming for responses that stream progress or large payloads.

Six steps to a reliable Amazon Quick MCP integration

1. Choose a deployment model

Decide between multi-tenant (shared) or dedicated per-customer endpoints.

Tip: Start with a hybrid: offer a shared control plane and dedicated runtime endpoints for customers with strict isolation needs.

2. Implement an MCP-compatible server

Support the MCP discovery (listTools) and invocation (callTool) flows and publish tool definitions in JSON schema. Aim for small, composable tools — agents prefer predictable, short-running calls.

Practical example: Expose a single high-value tool first (e.g., “searchCustomerRecords”) to validate the integration before expanding the toolset.

3. Implement authentication and authorization

Support user-level OAuth 2.0 (authorization code flow) for per-user actions and client_credentials for service-to-service calls. For demos, a no-auth endpoint is acceptable but should be rate-limited and not used for sensitive data.

Tip: Implement OAuth Dynamic Client Registration (DCR) so customers can auto-register clients and avoid manual allowlisting.

4. Document integration configuration for customers

Provide step-by-step admin instructions: redirect URIs to allowlist, required scopes, sample credentials, and where to find tool schemas. Clear documentation reduces onboarding friction and support requests.

Example redirect URIs that may need allowlisting (region variants):

https://quick.us-east-1.amazonaws.com/quick/oauth/callback
https://quick.eu-west-1.amazonaws.com/quick/oauth/callback
https://quick.onebox.amazonaws.com/quick/oauth/callback

5. Register the MCP integration in Amazon Quick

Customers add your MCP endpoint in the Quick console. Note: Quick treats the registered tool list as static after registration — changes to tool schemas require the customer to refresh or reauthenticate.

Tip: Version tools (v1, v2) rather than changing signatures in place. This keeps existing integrations stable.

6. Test and operate the MCP server

Validate protocol compliance with MCP Inspector (GitHub) and run production-level operations: tenant-aware logging, throttles, quotas, versioning, and credential lifecycle management.

Practical test checklist:

  • Run listTools and callTool flows with streaming.
  • Simulate token expiry and revocation.
  • Confirm behavior for operations exceeding 300 seconds (should return HTTP 424).

Transport, tooling and timeout realities to design for

  • HTTPS public endpoint: Required for Quick to reach your MCP server.
  • Streaming: Support SSE or HTTP streaming for long responses or progress updates; HTTP streaming is preferred when available.
  • JSON tool schema: Tool definitions must use JSON schema so agents understand inputs and outputs.
  • 300-second operation limit: MCP operations have a 300-second timeout. Calls exceeding that return HTTP 424; design short-running tools or async patterns.

Minimal JSON tool schema (example)

{
  "id": "create_invoice",
  "title": "Create Invoice",
  "description": "Generate and store a PDF invoice for an order",
  "input_schema": {
    "type": "object",
    "properties": {
      "orderId": { "type": "string" },
      "sendEmail": { "type": "boolean" }
    },
    "required": ["orderId"]
  },
  "output_schema": {
    "type": "object",
    "properties": {
      "jobId": { "type": "string" },
      "status": { "type": "string" }
    }
  }
}

Async job pattern (recommended for long-running tasks)

Have callTool return a job token and a status endpoint. Agents can poll or wait for a callback—keep the MCP invocation under 300s.

POST /callTool create_invoice -> 202 Accepted
Response:
{
  "jobId": "abc-123",
  "statusEndpoint": "https://api.example.com/jobs/abc-123"
}

GET /jobs/abc-123 -> 200 OK
{
  "jobId": "abc-123",
  "status": "completed",
  "result": { "invoiceUrl": "https://..." }
}

Authentication patterns and security considerations

  • User authorization (OAuth 2.0): Use authorization code flow for actions that require acting on behalf of a user. Support DCR to simplify customer onboarding.
  • Service authorization: client_credentials for automated agents or backend-only flows.
  • No-auth endpoints: Only for demos/public tools; enforce strict rate limits and do not return sensitive data.

Operational security: enforce short-lived tokens with refresh, implement credential rotation, and build token revocation hooks so customers can quickly cut access if needed.

Where to host or how to front existing APIs

  • Bring your own stack: Build an MCP server with an SDK and host it on AWS or elsewhere behind HTTPS.
  • AgentCore Runtime: Use Amazon Bedrock AgentCore Runtime to host MCP servers and agents.
  • AgentCore Gateway: Front existing REST APIs or Lambda functions and expose them as MCP-compatible tools. Integrate identity providers like Amazon Cognito, Okta, or Auth0 for auth flows.

Validate early with the MCP Inspector (GitHub) to check protocol compliance and streaming behavior before asking customers to register your integration.

Operate the MCP server like a production API

Treat the MCP endpoint as a first-class production surface area. Key operational practices:

  • Tenant-aware logging: Log tenant IDs, tool IDs, durations, and error codes to diagnose noisy neighbors and security incidents.
  • Throttles and quotas: Prevent runaway agent workloads; consider per-tenant and per-tool limits.
  • Versioning: Keep tool signatures stable; release new versions rather than mutate existing schemas.
  • Observability metrics: Track call volume, p95/p99 latency, failed calls by error code, and token refresh errors.
  • Metering and billing: Optional but useful for cost allocation or Marketplace offerings—measure per-call compute and downstream costs.

Suggested SLOs & metrics

  • p95 latency for callTool responses
  • Error rate (4xx/5xx) by tool
  • Average job completion time for async workflows
  • Number of token refresh failures per day

Real-world vignette: “Create Invoice” as an MCP tool

An ISV exposes “create_invoice” which generates PDFs and optionally emails them. PDF generation can exceed 300s, so the ISV designs an async pattern:

  1. callTool returns a jobId and statusEndpoint within the 300s window.
  2. Agent polls statusEndpoint or waits for a webhook callback from the ISV.
  3. When job completes, the agent fetches the invoice URL and updates the customer knowledge base.

Operational wins: the tool is easy to document, each call is bounded, and the ISV can meter and throttle heavy batches (e.g., mass invoice generation) to protect capacity.

High‑impact gotchas to watch

  • 300s timeout: Design async flows for long-running work; do not rely on the callTool invocation to complete extended processing.
  • Static discovery: Quick treats the registered tool list as static. Changes require customers to refresh or re-register; use versioning to avoid breaking integrations.
  • Streaming specifics: Test SSE and HTTP streaming thoroughly—partial payloads, reconnections, and timeouts behave differently across clients.
  • Noisy neighbors: Multi-tenant endpoints must enforce per-tenant quotas and priority controls to prevent one customer from impacting others.

Questions product and engineering teams should answer

  • What deployment model should we pick—multi-tenant or dedicated?

    • Choose multi-tenant for cost efficiency if you can enforce strong tenant isolation and quotas. Choose dedicated endpoints for customers with strict compliance or customization needs. Consider a hybrid approach to scale quickly and offer isolated options for premium customers.

  • How do we handle operations that exceed 300 seconds?

    • Return a job token and a status endpoint from callTool. Let agents poll or rely on callbacks/webhooks. Keep the MCP invocation responsive and move long-running work to backend jobs.

  • How do we ensure protocol compliance before customer onboarding?

    • Use MCP Inspector (GitHub) to validate MCP flows and tool schemas. Automate tests for streaming, token expiry, and error scenarios; include these checks in CI/CD.

  • What are the minimum operations we must implement?

    • Tenant-aware logging, throttles/quotas, versioning for tool schemas, credential rotation, and basic metering. Treat the MCP endpoint as a production API with SLAs and incident runbooks.

Resources & acknowledgements

  • MCP Inspector (GitHub)
  • AgentCore Runtime and AgentCore Gateway documentation (Amazon Bedrock)
  • OAuth 2.0 and Dynamic Client Registration (DCR) best practices
  • Guidance and contributors: Ebbey Thomas, Vishnu Elangovan, Sonali Sahu

Next steps: Run MCP Inspector against a dev endpoint, deploy a one-tool pilot (2–4 weeks), and measure agent-driven usage for 30 days to define SLOs, quotas, and pricing models.

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